The immediate takeaways from Figure REF  are that adding constraints help both algorithms, especially with limited data, but that even without constraints, MIOSR is more accurate. This is in slight contrast to the findings of [1]} where “the
constrained and unconstrained models have nearly identical
[\(R^2\) ] scores at all noise levels.” However, this is because we study a more difficult statistical regime, one with less data and with coefficients of different magnitudes. In particular, they used training sets that included 20 independent trajectories, hence the constraints did not add additional information.
